Specialized databases optimized for storing and searching vector embeddings using similarity search
A vector database is a specialized database designed to store, index, and search high-dimensional vector embeddings efficiently. Unlike traditional databases that search by exact matches, vector databases use similarity search to find the most relevant results.
Vector databases are essential for RAG systems because they enable fast, semantic search across large collections of documents, making it possible to find relevant information even when queries don't match exact keywords.
High-performance vector database written in Rust. Excellent for production use with both open-source and cloud options.
Open-source vector database with built-in ML models. Can run as a managed service or self-hosted.
Open-source vector database designed for scalable similarity search and AI applications.
Lightweight, embeddable vector database perfect for getting started quickly.
Library for efficient similarity search and clustering of dense vectors. More of a library than a full database.
Fully managed vector database with a generous free tier. Great for getting started without infrastructure management.
PostgreSQL extension for vector similarity search. Available in Supabase's free tier.
Managed Qdrant with a free tier. Same powerful engine as open-source Qdrant but fully managed.
Managed Weaviate with free tier options. Includes built-in vectorization capabilities.
Enterprise-grade managed vector database with advanced features, SLAs, and dedicated support.
Enterprise version of Milvus with additional features, support, and deployment options.
Enterprise features for Weaviate including advanced security, compliance, and support.
Amazon's managed search service with vector search capabilities, part of the AWS ecosystem.
Microsoft's search-as-a-service with vector search capabilities, integrated with Azure services.
Start with Pinecone or Supabase Vector for their free tiers and ease of use. No infrastructure to manage.
Qdrant or Chroma are excellent choices. Qdrant for performance, Chroma for simplicity.
Consider Pinecone Enterprise, Milvus Enterprise, or cloud provider solutions like AWS OpenSearch for compliance, SLAs, and support.
If you're already using PostgreSQL, Supabase Vector (pgvector) integrates seamlessly and avoids adding another database to your stack.
Understanding how vectors and similarity search work will help you choose the right vector database for your needs.
See how vector databases fit into RAG systems and our development methodology.